6 citations · 6 across the 5 of their papers we have counts for
3 papers · 1 filter
Generative Models as Adaptive Trust Regions for Discrete Bayesian Optimisation
Michael Dodds, Erik Bodin, Sebastian W. Ober +3
Bayesian optimisation (BO) is a powerful framework for sample-efficient optimisation of expensive black-box functions, but its application to high-dimensional discrete scientific d…
Variance reduction of diffusion model's gradients with Taylor approximation-based control variate
Paul Jeha, Will Grathwohl, Michael Riis Andersen +2
Score-based models, trained with denoising score matching, are remarkably effective in generating high dimensional data. However, the high variance of their training objective hind…
Reparameterization invariance in approximate Bayesian inference
Hrittik Roy, Marco Miani, Carl Henrik Ek +4
Current approximate posteriors in Bayesian neural networks (BNNs) exhibit a crucial limitation: they fail to maintain invariance under reparameterization, i.e. BNNs assign differen…